DETAILED ACTION This Office Action is in response to the application 18/ 239 , 255 filed on August 29 th , 202 3 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. Claims 1- 20 are pending and herein considered. Notice of Pre-AIA or AIA Status The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA. Information Disclosure Statement The information disclosure statement (IDS), submitted on 0 8 / 29 /202 3 , is in compliance with the provisions of 37 CRR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Claim Rejections - 35 USC § 102 In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status. (a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. Claims 1- 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Murdoch et al. ( Murdoch ), U.S. Pub. Number 202 0 /0 098466 . Regarding claim 1 ; Murdoch discloses a system for multi-factorial physiologically informed refreshment selection using artificial intelligence the system comprising a computing device, the computing device designed and configured to: retrieve a biological extraction pertaining a user, wherein the biological extraction contains an element of user data ( par. 0176; a smart health device to provide the biometric data to the machine learning algorithm. ) ; select a nutritional machine-learning model using the biological extraction ( par. 0206; operating a meal plan nutrition optimization algorithm involves retrieving at least one user profile from a profile database. ) ; determine a geolocation of the user ( par. 0225; identifies a geographic region and a geolocation from the spatiotemporal location through operation of the geofenced equivalence recommendations algorithm. ) ; identify one or more providers located within the geolocation of the user ( par. 0232; identify a user group classification to determine if an existing relationship exists between the users. ) ; generate a plurality of refreshment possibilities as a function of the one or more providers ( par. 0206; menu generation algorithm with user preferences from the at least one user profile; a selector to retrieve food items from at least one proximal food database for the menu generation algorithm, from the user preferences; operates the menu operation algorithm , configured by the user preferences. ) ; determine the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine-learning model, wherein the compatibility comprises a numerical score associated with a tolerance of the user ( par. 209; utilizes user location to determine the restaurants and the food distributors nearby the user as available food sources for generating meals; utilize the contents within the user pantry as available food sources; user available food source constraints may require a user to modify their meals on their menus based on the available food sources dependent on the user location; the available food sources may be identified as available foods from which the menu generator may select to generate or modify the meal plant menu. ) ; and display the compatibility of the plurality of refreshment possibilities ( pars. 0223 & 0230; the meal plant menu is display to the user device; displaying a partner location overlay with the meal plan menu within a user interface. ) . Regarding claim 2 ; Murdoch discloses the system of claim 1, wherein determining the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine-learning model further comprises determining the compatibility of the plurality of refreshment possibilities as a function of a user specification element ( par. 0074; the user meal preferences comprise food dislikes, food likes, food allergies or restrictions, meal and snack preferences including preferred recipes, nutrient targets, weight or other personal health objectives, preferred food brands, and grocer or food distributor preferences, kcal target and meal presets. ) . Regarding claim 3 ; Murdoch discloses the system of claim 1, wherein generating the plurality of refreshment possibilities as a function of the one or more providers comprises: receiving a recent refreshment selection, wherein the recent refreshment selection comprises one or more ingredients and wherein each refreshment possibility of the plurality of refreshment possibilities comprises the one or more ingredients ( par. 0061; food distributor refers to any purveyor (e.g., grocery store, grocery delivery service, etc.) that primarily offers ingredients to a user to utilize as the components of a meal, with the user size of the ingredient being greater than the quantity required for an individual meal portion ) . Regarding claim 4 ; Murdoch discloses the system of claim 1, wherein identifying the one or more providers within the geolocation of the user further comprises selecting one or more providers as a function of a dining option ( par. 0147; specify whether they want to eat the specific food item as part of every meal generated by the food menu algorithm, or whether there are specific meals (e.g., breakfast, lunch, dinner, snack) and which days that they would like to eat those specific food items, or identify that the food can be considered when it can be purchased within financial budget guidelines. ) . Regarding claim 5 ; Murdoch discloses the system of claim 1, wherein determining the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine-learning model further comprises determining compatibility as a function of a user body measurement ( par. 0082; determine the footprint of the food item to best fit the requirements of the meal and to manage the process of verifying that the food item substitution is a better fit for the meal. ) . Regarding claim 6 ; Murdoch discloses the system of claim 1, wherein the computing device is further configured to rank the plurality of refreshment possibilities as a function of the compatibility of the plurality of refreshment possibilities ( par. 0232; based on the user group classification and the group event classification, the user preferences from a user preference database, the food source selector may select relevant proximal food databases to all the users to generate a group menu. ) . Regarding claim 7 ; Murdoch discloses the system of claim 1, wherein determining the compatibility of the plurality of refreshment possibilities utilizing the biological extraction and the nutritional machine-learning model further comprises determining compatibility as a function of a nutrient score ( par. 0090; set nutrient targets and preferences as well as food items they want to include or exclude from their weekly menu of meals; populates individual food items based on component categories to build meals that fit the user’s target preferences within a narrow margin. ) . Regarding claim 8 ; Murdoch discloses the system of claim 1, wherein determining the compatibility of the plurality of re freshment possibilities utilizing the biological extraction and the nutritional machine-learning model further comprises determining the compatibility of the plurality of refreshment possibilities as a function of a modified ingredient list ( par. 0089; a meal plan nutrition optimization algorithm takes into account the preferences, dietary restrictions, health objectives, and financial budget set by the individual users and generates a single meal, series of meals, or 1-n day meal plan for the user. ) . Regarding claim 9 ; Murdoch discloses the system of claim 9, wherein the computing device is configured to generate the modified ingredient list as a function of a user specification element ( par. 0061; food distributor refers to any purveyor (e.g., grocery store, grocery delivery service, etc.) that primarily offers ingredients to a user to utilize as the components of a meal, with the user size of the ingredient being greater than the quantity required for an individual meal portion. ) . Regarding claim 10 ; Murdoch discloses the system of claim 1, wherein the computing device is further configured to: receive a selection of one or more refreshment possibilities of the plurality of refreshment possibilities (par. 0215; receives information from the linked user services associated with the meal plan menu and the user profile, a historic user interactions database and a global user interaction database.) ; and transmit the one or more selected refreshment possibilities to a user database ( par. 0216; retrieving a meal framework comprising at least one food component category from a meal framework database through operation of a meal selector configured by a preferences profile in a user profile. ) . Regarding claim 11 ; Claim 11 is directed to a method which has similar scope as claim 1. Therefore, claim 11 remains un-patentable for the same reasons. Regarding claim s 1 2-20 ; Claim s 1 2-20 are directed to the method of claim 11 which ha ve similar scope as claim s 2-10 . Therefore, claim s 1 -20 remain un-patentable for the same reasons. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to FILLIN "Examiner name" \* MERGEFORMAT KHOI V LE whose telephone number is FILLIN "Phone number" \* MERGEFORMAT (571)270-5087 . The examiner can normally be reached FILLIN "Work Schedule?" \* MERGEFORMAT 9:00 AM - 5:00 PM EST . Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. 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If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /KHOI V LE/ Primary Examiner, Art Unit 2436